Forecasting Chili Prices in Metro City Using Long Short-Term Memory (LSTM)
DOI:
https://doi.org/10.35870/ijsecs.v5i1.3526Keywords:
Cayenne Pepper, Price Prediction, Long Short-Term Memory, Historical Data, Metro CityAbstract
Cayenne pepper is one of the important commodities in the staple market in Indonesia which has a vital role in people's daily lives. Fluctuations in the price of cayenne pepper are often a challenge that impacts farmers and consumers, causing uncertainty in production and distribution planning. This research aims to develop a cayenne pepper price prediction model using the Long Short-Term Memory (LSTM) method, utilizing historical data from the data.metrokota.go.id portal for the period October 2023 to October 2024. By using LSTM, this model successfully captures long-term patterns in cayenne price data, with a Final Validation Loss of 0.00249 which indicates a high level of accuracy. The prediction results are expected to help farmers determine the optimal selling time, traders in managing stocks efficiently, and policy makers in formulating strategies to mitigate the impact of price fluctuations. In addition, this study highlights practical implications for stabilizing commodity markets, particularly in Metro City, as well as the relevance of these findings to be applied to other agricultural commodities.
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